The seminal work in [1]} studies Bayesian deep networks for multiple computer vision tasks.
Based on this work, [2]}, [3]} produce and analyze epistemic and aleatoric uncertainties in 3D object detection.
However, they do not use the predicted uncertainties to significantly improve the performance of the detector.
In this paper, we focus on estimating the aleatoric uncertainty by way of predicting a probability distribution over box detections, and we leverage the predicted distribution to combine and refine our detections.
The epistemic uncertainty is not estimated by our method since it currently cannot be computed efficiently.
